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Summary artificial intelligence in practice- part-4

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Summary artificial intelligence in practice- part-4

  1. 1. Some Impressionistic Take away from the Book of Bernard Marr & Matt Ward Artificial Intelligence in Practice ( Part – 4) ( How 50 Successful companies used AI & Machine Learning to Solve problems) Ramki ramaddster@gmail.com
  2. 2. The Summary of this book is made in 4 parts due to large coverage of the book . This is Part – 4 ( Read this after Part-1 , 2 & 3)
  3. 3. Part – 4 Services , Financial & Healthcare Companies
  4. 4. Using AI to Detect Fraud & Improve customer Experience
  5. 5. American Express  With 110 million AmEx cards in operation and more than one trillion dollars in transactions processed.  American Express handles more than 25 percent of U.S. credit card activity.  The company interacts with people on both sides of transactions – millions of businesses and millions of buyers – giving American Express a rich flow of data to leverage. How American Express uses Big Data in practice  Data analytics, and specifically machine learning, is at the heart of American Express’s decision making. Two areas where this is evident are detecting fraud, and bringing merchants and customers closer together.  Credit card fraud detection and prevention now relies heavily on machine learning algorithms.  AmEx’s goal is to detect fraudulent transactions as quickly as possible to minimize loss, so they employ a machine learning model that uses a variety of data sources, including card membership information, spending details, and merchant information to detect suspicious events, and make a decision in milliseconds by comparing that event to a large dataset.
  6. 6. American Express  This has enabled American Express to detect more fraudulent transactions and save millions. Using Big Data and Machine Learning algorithms for fraud prevention has now become commonplace in the industry.  Visa also uses this technology and checks many hundreds of aspects of any transaction in near-real-time.  According to Visa’s estimates, this approach has identified $2 billion in potential annual incremental fraud opportunities – which the company was able to sort out before any of it was lost.  Interestingly, American Express is increasingly moving away from focusing on its traditional function of providing credit for consumers and merchant services for processing transactions, and towards actually making the connection between the consumer and the business that wants to reach them.  For consumers, the company is using its vast data flows to develop apps that can connect a cardholder with products or services.
  7. 7. American Express  One app looks at past purchase data and then recommends restaurants in the area that the user is likely to enjoy.  Another, called Amex Offers, shows real-time coupons relevant to the individual’s lifestyle and buying habits based on their physical location and nearby businesses. And this isn’t just a benefit to cardholders that use the app, but also hopefully an incentive for more businesses to accept American Express.  On the merchant side, American Express is offering new online business trend analysis and industry peer benchmarking to help companies see how they’re doing compared to their competition. The data is anonymized, meaning any personally identifiable data is stripped out of the transactions, but merchants are able to see detailed trends within specific niche markets or customer segments. The technical details  In 2010, American Express upgraded from traditional database technology to a Hadoop infrastructure and brought in machine learning algorithms. The company is so serious about embracing the data side of its business that it has now opened a tech lab in Palo Alto, California, specifically to focus on Big Data, cloud computing and mobile infrastructure.
  8. 8. Results, Key Challenges, Learning Points & Takeaways Real time analysing transactions using Machine Learning algorithms leads to detection of fraudulent transactions. Less chances of false positives occurring, reducing inconveniences to customers- Trust building . Machine learning models for detecting fraud need to constantly adapt & update themselves in real time, meaning they need a consistent flow of data to learn from . Distributed storage and large amount of compute power are needed to handle the amount of data that is needed to make accurate predictions in real time. Overall security improvement through small increases in efficiency.
  9. 9. Using AI to Improve Medical decisions & Scientific Research
  10. 10. Elsevier Global multimedia publishing business – offers 20000 products for educational & professional science & healthcare communities. First stage digital transformation – digitization of huge amount data published in reports & journals – 140 years of history. AI being used to draw new insights from this data. Two patients of same age & gender will present to their primary healthcare practitioner with the same symptoms, and yet there will be a huge variation in the outcome & cost of treatment they receive. This is due to diagnosing and treating are by different health care staff with different levels of knowledge & experience. Developing AI derived pathway from initial presentation to examination to treatment procedures and prescribing of medication, patients are more likely to get better quicker, and reduction in cost.
  11. 11. Elsevier-Clinical Decision support  When considering how to make AI work for Elsevier and its customers, as Its mission here is to build what are described as “clinical decision support” systems.  These use information gathered from Elsevier’s archives, combined with patient medical data and financial claims data, to suggest the best course of action – known as a “pathway” – for treating specific patients.  The next step of the process is to augment these decision support systems with machine learning and deep learning.  This should mean more accurate predictions, more efficient treatment and ultimately better patient outcomes.  The large amounts of data increase predictability and produce more accurate models with better inferences  You get a much more accurate understanding of individual patients by bringing together data their clinical history, their claims data, genomic data, etc.  Elsevier’s technology is now in use at 2,000 oncology centers at healthcare facilities across the US, where it informs decisions on patient healthcare every day, and the adherence rate, i.e. the degree at with doctors follow that advice, is very high.
  12. 12.  “We were able to create these neural networks of closed loops, and we trained these predictive models against large patient databases … so we built an application what generates a differential diagnosis based on that model.  It gives you a weighted differential, it says, with those symptoms in a person of this age and gender, there’s a 70% chance it’s this, a 35% chance it’s that.”  “Neural networks” refers to a technology designed to mimic the learning characteristics of the neurons in the human brain.  Data is passed between neurons which essentially each ask a different question about it.  The results of all of those questions are aggregated into a single output – representing the answer to the question that the neural network is designed to solve. . Elsevier-Neural Network
  13. 13. Results, Key Challenges, Learning Points & Takeaways The adherence rate of 85% among clinical staff to the treatment pathways suggested by its Via oncology platform. Elsevier amalgamates patient medical records, insurance claims & billing data & published medical literature to predict which treatment pathways are most likely to be effective. Treatment can be standardized if machines are used to determine optimal treatment paths dependent on the patient’s details, medical history and the symptoms they present with. Standardized treatments lead to better patient outcomes if they can be optimized according to the data, and also help healthcare providers to reduce overall cost.
  14. 14. Using AI to Combat the US $ 450 B Counterfeit Industry
  15. 15. Entrupy  Uses AI to combat counterfeit goods .  Provides a platform as a service to brands to reduce the revenue loss due to counterfeits.  Focus area counterfeiting through machine learning .  Sales of counterfeit goods is close to half trillion US $ annually.  Diluting brand identity.  Eats into business of genuine resellers and wholesalers.  Entrupy developed scanning technology using machine learning and deep learning techniques – which detects whether items are genuine- cloths , accessories, jewelry, electrical goods and automobile parts.  Service users can use a phone app or dedicated handheld scanner to check their purchases.  Microscope cameras that are able to record the tiniest details of product’s construction .  This technology is able to discern even : Super fakers” very high quality replica that are impossible for human to differentiate from a genuine product.
  16. 16. Technology used  Database of millions of images of products sold by the different brands- Chanel, Dior, Burberry, Gucci, Louis Vuitton & Prada.  Specialized microscope lenses to capture micro details  The images of the genuine products are used to train convolutional neural network algorithms to classify images based on texture.
  17. 17. Results, Key Challenges, Learning Points & Takeaways System had a 98.5% rate of correctly identifying counterfeit merchandize. Technology has enabled brand guarantee and confidence to the customers . AI can parse image data in incredibly high detail far more quackery than a human eye could, and determine between counterfeit products and genuine items. Brands are happy to help with putting this technology into the hands of customer and resellers if it helps protect their revenue and perceived value.
  18. 18. Using AI to make Mortgage Simpler
  19. 19. Experian  One of the largest consumer credit reference agencies.  Businesses, Banks and financial institutions rely on this company  They 3.6 petabytes of data on people and their spending habits.  Apply AI to this data to make accurate predictions.  Like all financial services, they are being rapidly changed by waves of technological innovation sweeping through industry – none more so than AI & Machine learning .  Mortgage – time consuming & complex. Coordinating information between –Buyers, Sellers, Surveyors, Estate agents, Solicitors, Underwriters, Mortgage brokers & lenders.  Buying a property is stressful process.  Work gets duplicated between agencies due to inconsistencies in the way information is transferred.
  20. 20. Experian  AI is used for analyzing thousands of applications to determine improvement in efficiencies , reducing the duplicates & streamlining workflows.  System is trained to look at every data, frequency of use .  Practically impossible for human beings to carry out other than small historic sample data.  Predictive technology for limited credit histories to obtain mortgages or personal loans.  Assessment by lenders and comparisons of profiles to come out with trustworthy decisions.  Machine learning is used for processing of data across WF & builds models about what data is valuable, what is surplus at each stage of the process.  Experian has built a platform –Analytical Sandbox- allows to produce on demand data for insights.  Also uses Cloudera’s Enterprise platform to enable quick access to big data.
  21. 21. Results, Key Challenges, Learning Points & Takeaways  Approval mortgage applications will be reduced to day from weeks and months.  Data driven decisions.  Credit reference agencies are positioned to streamline workflows across complex procedures .  AI examines every aspect of the workflow , tracks in details across instances .  Smart businesses are learning that repackaging data, processing it with ML & offering it as a service is a great way of diversifying their range of services in the age of AI.  Technology not ahead of security of data.
  22. 22. Using AI to Increase Sales
  23. 23. Harley-Davidson  An US Manufacturer of motorcycles- Sells around 1,50,000 bikes per year globally.  Licenses its iconic brand for use of clothing, homeware & accessories.  When we think about effective marketing for a Harley-Davidson dealership, the first thing that pops into our mind is probably not the ways you can use artificial intelligence to ramp up your results. It’s a good thing the owner of New York City’s Harley dealership, Asaf Jacobi decided to give AI a try, because it increased the dealership leads by 2,930% in just 3 periods.  That is a remarkable number for a start-up, but for an established brand such as Harley-Davidson, that was extraordinary.  Although Jacobi had started researching options to boost sales at his dealership in the off-season and came across some AI tools for marketing and advertising, it was his chance meeting with Or Shani, the CEO of AI firm Algorithm, which had an AI-driven marketing platform called Albert that convinced Jacobi to give it a try.
  24. 24. Harley-Davidson-How Albert works  The first test of Albert was a weekend promotion called "48 Bikes in 48 Hours."  They sold 15 motorcycles that weekend, nearly doubling the summer sales record of eight bikes sold in one weekend.  Albert used business logic, the KPIs available for Harley-Davidson NYC and past campaign performance to identify unknown audiences, the best budget allocation across digital channels and even evaluate the performance of different word choices or colors on the creative.  Albert processed the data it had been given to figure out trending behavior. It continued to optimize the marketing and ad performance as new data continued to come in.  Albert executes digital ad campaigns autonomously and adjusts them automatically based on performance.  It can figure out the audiences that are most likely to convert, compare platforms and implement learning across platforms, and discover what creative worked better.  AI can do this exponentially faster than humans, in virtually real-time.
  25. 25. Harley-Davidson-How Albert works Here’s what Albert was able to do for Harley-Davidson NYC:  The dealership credits Albert with 40% of its motorcycle sales over a six- month period.  In just three months, they had an increase of 2,930% in leads, with 50% of those being from lookalikes, prospects with similar buying patterns and preferences as those likely to purchase Harley-Davidsons. This insight opened up an entirely new audience that they had previously not marketed to.  Albert also discovered that Facebook ads converted 8.5 times higher than other Harley ads, so they focused their advertising efforts only on platforms that worked. New factory optimized for more expensive workforce  Unlike many manufacturers, when you walk into the Harley-Davidson plant in York, Pa., you will still find human workers assembling the iconic motorcycles by hand.  Although the factory was redesigned, it still has people everywhere. Workers operate in teams of five or six to build each bike. Since virtually every bike is one-of-a-kind, humans are uniquely qualified to adjust on the fly when it's necessary to create these customized bikes.
  26. 26. Results, Key Challenges, Learning Points & Takeaways  Data on customer behaviour – Predictions are more accurate.  Automated segmentation & targeting of customers can often result in uncovering entire demographics that a business has never considered marketing to, but in fact make great customers.  Identify more effective channels- email, social media, display advertising – assigning of resources where probability says they will provide the best return.  AI boosted selling is no longer the preserve of the big tech companies, thanks to new generation of “ as-a-service” platforms for targeting & selling to new customers.
  27. 27. Using AI to Travel for Less
  28. 28. Hopper  Mobile app-based platform that uses Machine learning & uses huge volumes of historical flight data to predict the best time to buy flights.  Launched in 2015 & in 2017 $ 1 million worth of flights everyday . Sales approaching one Billion a year.  Scanning price comparison sites to find the best price for holiday flight, weekend deals.  Removed the middle man from the process.  Replaced old human travel agent with an AI travel agent.  Users tell it where they want to travel to with rough idea of date- Hopper gives the best prices it can find.  Users also get a prediction – it will also tell if they can wait for a better price.  It is like walking into a shop, being persuaded not to buy yet, but to wail until prices come down.  Predictive model gives Hopper the competitive advantage.
  29. 29. Hopper-Technology & Tools  Built and trained predictive algorithms using data in from global distribution operators.  This data was generally considered less valuable, it was able to negotiate a good price.  Prices likely to change based on flow of demand.  Hopper augmented the data with information about customers- Close proximity to more than one airport and potential saving if they fly out of different airports.  Alternate destinations to planned destinations.  For e.g. – Someone searching for flights to Rome, do they really just want to visit Italy ?  Suggestion thrown up for Milan or Naples mixed in with their results
  30. 30. Results, Key Challenges, Learning Points & Takeaways  Fourth largest downloaded travel app after Uber, Lyft and Airbnb with over 20 million users.  Able to predict the cheapest time for its users to buy flights anywhere in the world with 95% accuracy – average saving of US $ 50 every flight.  20% of the $ 500 million it had taken bookings came from selling flights that customers had not even searched for directly.  User friendly search criteria  AI can replace many of the old-fashioned “ middle men” roles – travel agents, doing the same work at much larger scale and reduced costs.  Machine learning prediction can accurately find cheaper flights & reduce the stress and fear of missing out inherent in price comparison site searches.
  31. 31. Infervision- Using AI to Detect Cancer & Strokes
  32. 32. Infervision  Chinese computer vision specialist has applied the technology to potentially save millions of lives from life threatening diseases.  Use AI to interpret visual data.  Image recognition technology (the same sort of thing used by Facebook for facial recognition or Google in image searches) is one of those tasks that’s ideally suited to AI, particularly Deep learning .  Now, the technology is quickly progressing to a point where loftier ambitions, like saving lives, are being realized.  Lung cancer is the leading cause of death in China, claiming the lives of more than 600,000 people every year, largely because of air pollution.  With lung cancer, CT scan images are examined by radiologists to spot signs of cancer as early as possible. However, in a country with a real dearth of doctors, especially qualified radiologists, this can mean radiologists wading through hundreds of scans each day.  This is time-consuming, laborious, and, frankly, quite tedious. Simple human error, often caused by fatigue, means mistakes being made and important diagnoses being missed.  This problem inspired Chen Kuan, founder of medical image diagnostics startup Infervision, to focus his work with deep learning and image recognition on the world of medicine.
  33. 33. Results, Key Challenges, Learning Points & Takeaways  Partnership with over 200 hospitals around the world.  Technology is being currently being used to analyse 20000 scans a day.  AI will lead to a digital shift in traditional medical imaging, requiring AI & people to work together to meet the challenges of the medical industry.  Deep neural enable computer algorithms to become increasingly efficient at sorting images.  New value can be extracted from old data with cutting edge technology such as deep learning .  It will not replace doctors, but rather enable them to work far more quickly & efficiently than they previously could
  34. 34. Using AI to Cut down the “ False Declines”
  35. 35. MasterCard  Processed Billion of transactions – crucial link between thousands of banks and millions of merchant establishments.  Year 2017 acquired Brighterion to complete its mission of rolling out AI technology across its network.  Having a card transaction declined at the checkout can be a frustrating and embarrassing occurrence.  So much so that it can seriously damage brand loyalty – according to research by MasterCard, a third of us have withdrawn our custom from a retailer due to our cards being refused.  Often this is due to the transaction being incorrectly flagged as fraudulent in some way – the algorithms which make the call on whether a payment is valid have erred on the side of caution, and sometimes they get it wrong.  Aside from the inconvenience, it causes us, the cost to businesses and the wider economy of these false declines is around $118 billion – an amount 13 times higher than the cost of actual card fraud.
  36. 36. MasterCard – Real time Analytics  The quantum leap in the ability to both detect fraud and reduce false declines has come about through its acquisition of California-based artificial intelligence specialists Brighterion.  Technology developed with Brighterion has enabled it to move to analyzing data in real time.  Machine learning algorithms must be incredibly efficient to handle the 75 billion transactions per year happening at 45 million global locations, which are processed by the MasterCard network.  Today, the decisions of whether or not to decline a transaction are based on a constantly flowing stream of data, and self-teaching algorithms, rather than a static sample dataset and fixed rules, which has had impressive results.  The artificial intelligence systems, because they are self-learning, are always current and there is no longer a learning lag happening.  “What it does is goes through billions of transactions and figures out what is the propensity of the transaction being fraudulent, and it gives this advice to the bank in the system, when the transaction goes through for authorization.
  37. 37. MasterCard – Real time Analytics  The system uses a real time stream of transactional data, along with external data including anonymized and aggregated customer information, and geographical information.  Geographical information is highly useful because not only does it give an overview of the types of transactions which are “normal” for a particular area, it also reveals what patterns of fraudulent activity are associated with it. Again, all of this information is aggregated in real time as it happens.  This means that patterns of fraud – which is often carried out at large scale by organized gangs, who will target businesses in a particular location, or attempt to “cash out” at ATMs spread across a city - can be detected, tracked and stopped.
  38. 38. MasterCard – The Challenges of AI  Building smart, automated systems has been a core strategy at MasterCard for many years, but the acquisition of Brighterion and the incorporation of its technology into MasterCard systems has been a move towards “pure” AI.  Many areas of its business, from customer service to anti-money- laundering measures, are set to benefit from an AI overhaul.  One key challenge has been ensuring a consistently high quality of data – as errors in transaction records or other data stores will inevitably lead to even the smartest machines making bad decisions.  Company’s success with this down to the more than 50 years’ experience it has at generating and verifying transactional records – “We have been doing it for many, many years”.  A second challenge is determining the priorities when it comes to making decisions on where in the business to deploy potentially costly AI infrastructure.
  39. 39. Results, Key Challenges, Learning Points & Takeaways  Detecting the fraud detection has increased by 3 folds.  False positives has been reduced by 50%.  Decision based on static datasets using fixed rules is not sufficient for fast, hassle-free fraud verification over a network of MasterCard's scale.  Datasets & predictive models that update in real time allow for far more accurate predictions about the legitimacy of a transaction, meaning fewer false declines.  Talent acquisition became a challenges when the decision was made to implement AI . By acquiring Brighterion this challenge was mitigated.  Data quality is utmost importance – inaccurate data would lead to a potentially even greater number of false positives, or fraudulent transactions being incorrectly approved.
  40. 40. How AI Helps Businesses understand their Customers
  41. 41. Salesforce  Salesforce – one of the world’s leading CRM solution provider.  Product & services address the business growth and track relationships with customers.  Founded in 1999- Concept of software as-a-service ( Saas) over internet.  Business challenges – Maintaining customer relationship – old fashioned mail shots to social media & chatbots, acquire and retain customers across different part of the globe.  Salesforce offers its customers –Einstein platform, which it calls the world’s AI solution for CRM.  Cloud hosted CRM solution.
  42. 42. Salesforce  There are many ways that companies can use machine learning in their sales process. Here are just a few of the possibilities:  Interpret customer data: ML helps make sense of the data we collect about our customers. Research shows how important it is to have a “data-driven understanding” of our customers.  Even though many organizations have systems and spent resources to gather and store customer data, it’s the machine learning that will now help us make effective use of that data in ways that relying on humans alone could not.  Improve sales forecasting: When you gather data on your prospect (company size, stakeholders, solutions they want) and then through machine learning have the ability to compare it to historical sales efforts, you can connect the dots and better predict what solutions would be effective and the likelihood of the deal closing and how long it will take. This insight helps sales management better allocate resources and predict sales projections.
  43. 43. Salesforce  Predict customer needs: Business success relies on how well we provide what our customers need. Machine learning can improve how responsive and proactive we are to anticipate the needs of our customers. The better we are in sales at addressing our clients’ needs before they get escalated and at suggesting a solution that could help make their life better and easier, the stronger our relationship will be. Machines won’t forget to follow-up or be too busy to proactively share solutions.  Efficient transactional sales: According to HBR, by 2020, customers will manage 85% of their interactions with an organization without interacting with a human. Having machines step in to handle certain sales efforts quickly and effectively can free up the human sales force to focus on the relationship.  Sales communication: There will most likely be dramatic changes to sales communication as a result of machine learning. If business communication mimics the transformation of consumer communication, the business equivalent of short-form communication such as tweets and text messages will be AI responses. Machines can quickly and easily answer queries about pricing, product features or contract terms. Within the next decade, virtual reality would allow prospects to tour a factory, “join” in to conferences and meetings with your entire team and see products being manufactured, all without leaving their own office.
  44. 44. Results, Key Challenges, Learning Points & Takeaways  With Einstein, Salesforce has effectively positioned itself as the first provider of AI- as-a-service for CRM.  Delivering AI as –a-service has the potential to drive strong economic growth by empowering businesses of any size to take advantage of these powerful tools & technologies.  CR can effectively by managed in an automated way via ML by algorithms that can learn the most effective approaches to marketing & managing relationships with individuals according to the profile they fit.  Salesforce makes data ownership a unique selling point of their service , meaning that their customers do not have to let their valuable customer-data out of their hands to take advantage of its cloud-based services.
  45. 45. Using AI to do Everything
  46. 46. Uber  Business Model around disruptive data – pairing public hire drivers with passengers by correlating location data from both parties smartphones.  Connecting waiting passengers far more quickly than conventional taxi operators.  Uber has invested heavily in AI- AI First company – end to end of the business. AI helping to serve  How to serve customers with minimum expense through driver wages & mileage.  Speedy response for the ride request- competitive edge .  Dealing with passengers who are drunk & abusive – Night rides. How AI used in Practice  AI is used as a core business – Connecting & dispatching drivers to passengers for pick up – Most efficient route.  Power company’s “ Surge Pricing” model – Based on supply & demand – encourage drivers to clock in- reducing customer wait time.
  47. 47. Uber How AI used in Practice  Hotel, Airlines & public transport have used this technique balancing supply & demand- peak pricing .  Predictive technology to adjust pricing in real time.  Using AI analyzing the pick up point & destination-Business or personal and suggest which account to be used if there are two accounts.  Uses Machine learning algorithms to segments customers for marketing programs , promotions , track frequency of opening the app etc.  Recent patent application reveals that Uber has developed a technology to predict whether a customer may be drunk.  Uses Machine learning within its Uber Eats food delivery platform.  Can predict how long it will take a customer food to arrive.
  48. 48. Uber Technology  Uber uses GPS data from passengers & drivers smart phone & map data to plan routes between two points.  Data gathered from the journeys made is fed back into learning algorithms with the aim of giving customers more accurate ETA for their rides- shortening the waiting time.  Suggests the passengers a shorter waiting time for pick up depending on the traffic condition- nearby location- so that customer can move to that location instead of waiting.  Machine learning platform – Michelangelo – Data lake where it logs all of its transactional and customer behavior data.  Uber’s current AI research division – Uber AI labs was formed and acquired Geometric Intelligence in 2016.  Conducts research on deep learning & neural networks- going beyond Uber business cases.
  49. 49. Uber – Workflow – Value Proposition
  50. 50. Results, Key Challenges, Learning Points & Takeaways  Shorter waiting time for rides & efficiently routed journeys leading to customer satisfaction.  Customer retention – high lifetime value to business.  Success with Machine learning & predictive models- Scaling up & global reach out.  Machine learning has been applied to all areas of business – operational efficiencies & customer service.  Uber has disrupted the traditional taxi hire business globally.
  51. 51. Mail your comments to ramaddster@gmail.com End of Part -4 Will continue the summary in Part -5